Evaluation of doxorubicin and ß-lapachone analogs as anticancer agents, a biological and computational study.

We have conducted an experimental and computational evaluation of new doxorubicin (4a-c) and ß-lapachone (5a-c) analogs. These novel anticancer analogs were previously synthesized, but had not been tested or characterized until now. We have evaluated their antiproliferative and DNA cleavage inhibition properties using breast (MCF-7 and MDA-MB-231) and prostate (PC3) cancer cell lines. Additionally, cell cycle analysis was performed using flow cytometry. Computational studies, including molecular docking, pharmacokinetic properties, and an analysis of DFT and QTAIM chemical descriptors, were performed to gain insights into the electronic structure and elucidate the molecular binding of the new ß-lapachone and doxorubicin analogs with a DNA sequence and Topoisomerase II (Topo II)α. Our results show that 4a analog displays the highest antiproliferative activity in cancer cell lines by inducing cell death. We observed that stacking interactions and hydrogen bonding are essential to stabilize the molecule-DNA-Topo IIα complex. Moreover, 4a and 5a analogs inhibited Topo's DNA cleavage activity. Pharmacodynamic results indicated that studied molecules have favorable adsorption and permeability properties. The calculated chemical descriptors indicate that electron accumulation in quinone rings is relevant to the reactivity and biological activity. Based on our results, 4a is a strong candidate for becoming an anticancer drug.

Modeling Adsorption of CO2 in Rutile Metallic Oxide Surfaces: Implications in CO2 Catalysis.

CO2 is the most abundant greenhouse gas, and for this reason, it is the main target for finding solutions to climatic change. A strategy of environmental remediation is the transformation of CO2 to an aggregated value product to generate a carbon-neutral cycle. CO2 reduction is a great challenge because of the large C=O dissociation energy, ~179 kcal/mol. Heterogeneous photocatalysis is a strategy to address this issue, where the adsorption process is the fundamental step. The focus of this work is the role of adsorption in CO2 reduction by means of modeling the CO2 adsorption in rutile metallic oxides (TiO2, GeO2, SnO2, IrO2 and PbO2) using Density Functional Theory (DFT) and periodic DFT methods. The comparison of adsorption on different metal oxides forming the same type of crystal structure allowed us to observe the influence of the metal in the adsorption process. In the same way, we performed a comparison of the adsorption capability between two different surface planes, (001) and (110). Two CO2 configurations were observed, linear and folded: the folded conformations were observed in TiO2, GeO2 and SnO2, while the linear conformations were present in IrO2 and PbO2. The largest adsorption efficiency was displayed by the (001) surface planes. The CO2 linear and folded configurations were related to the interaction of the oxygen on the metallic surface with the adsorbate carbon, and the linear conformations were associated with the physisorption and folded configurations with chemisorption. TiO2 was the material with the best performance for CO2 interactions during the adsorption.

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors.

A series of SARS-CoV-2 main protease (SARS-CoV-2-Mpro) inhibitors were modeled using evolutive grammar algorithms. We have generated an automated program that finds the best candidate to inhibit the main protease, Mpro, of SARS-CoV-2. The candidates were constructed based on a pharmacophore model of the above-mentioned target; relevant moieties of such molecules were modified using data-basis sets with similar chemical behavior to the reference moieties. Additionally, we used the SMILES language to translate 3D chemical structures to 1D words; then, an evolutive grammar algorithm was used to explore the chemical space and obtain new candidates, which were evaluated via the binding energy of molecular coupling assays as an evaluation function. Finally, sixteen molecules were obtained in 3 runs of our program, three of which show promising binding properties as SARS-CoV-2-Mpro inhibitors. One of them, TTO, maintained its relevant binding properties during 100 ns molecular dynamics experiments. For this reason, TTO is the best candidate to inhibit SARS-CoV-2-Mpro. The software we developed for this contribution is available at the following URL: https://github.com/masotelof/GEMolecularDesign.